Searching an electronic medical record

ABSTRACT

A method, system, and medium are provided for searching an electronic medical record. Search results are returned in response to a search query. The search query may be one or more designated medical concepts. The search results may be displayed according to a ranking that determines which search results are likely to be the most responsive to a query submitted by a particular clinician based on matching the most important clinical concepts in each document to the most important clinical concepts in the search query.

BACKGROUND

Clinical facilities (e.g., hospital, therapy center, practice group) orother managers of medical information may maintain a patient's medicalrecord in an electronic database. An individual patient's medical recordis called an electronic medical record or personal health record. Theelectronic medical record may include electronic documents and databaseentries. Over time, a patient's electronic medical record may contain alot of information. The information may be reached by browsing throughthe electronic medical record and opening documents to look forinformation. The large amount of information makes it difficult to finddesired information.

SUMMARY

Embodiments of the invention are defined by the claims below, not thisSummary. A high-level overview of various aspects of the invention areprovided here for that reason, to provide an overview of the disclosure,and to introduce a selection of concepts that are further describedbelow in the Detailed Description. This Summary is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in isolation todetermine the scope of the claimed subject matter.

In a first aspect, one or more computer-storage media havingcomputer-useable instructions embodied thereon for performing a methodof finding information in an electronic medical record are provided. Themethod includes receiving a search query from a user to search theelectronic medical record, wherein the electronic medical record isassociated with a patient. The electronic medical record includes aplurality of electronic documents that describe a medical history forthe patient and is stored on the computer-storage media. The method alsoincludes identifying one or more components of the electronic medicalrecord that contain text that matches the search query, wherein each ofthe one or more components is a section of text with in the electronicmedical record that includes one or more words. The method also includesdetermining a query-responsiveness score for each of the one or morecomponents that match the search query. The query-responsiveness scoreindicates how responsive an individual component is to the search query.The method further includes presenting search results that communicateinformation describing each of the one or more components. The searchresults are displayed ordered according to the query-responsivenessscore assigned to each of the one or more components.

In a further aspect, one or more computer-storage media havingcomputer-useable instructions embodied thereon for performing a methodof searching an electronic medical record for a selected clinicalconcept are provided. The method includes receiving a search query thatincludes a clinical concept from a user. The clinical concept is anaspect related to a person's health. The method also includesidentifying one or more uses of the clinical concept in the electronicmedical record, wherein the electronic medical record is an electronicdescription of a medical history for a patient and is stored on thecomputer-storage media. The method further includes determining aquery-responsiveness score for each document within the electronicmedical record in which the clinical concept is used. Thequery-responsiveness score describes how important a particular documentis likely to be to the user. The query-responsiveness score isdetermined based on a clinical-importance score for each of the one ormore uses of the clinical concept and at least one boost factor that isbased on the search query and the user. The method also includespresenting search results based on the query-responsiveness scoreassociated with said each document within the electronic medical recordin which the clinical concept is used.

In a further aspect, one or more computer-storage media havingcomputer-useable instructions embodied thereon for performing a methodof preparing an electronic medical record for electronic searching areprovided. The method includes receiving the electronic medical recordthat includes information describing at least a portion of a medicalhistory associated with a patient. The method also includes identifyingclinical concepts within the electronic medical record, wherein theclinical concept is an aspect related to a person's health. The methodalso includes, for each of the clinical concepts, determining apatient-subject status for each use of the clinical concept in theelectronic medical record, wherein the patient-subject status indicateswhether the patient is a subject of a particular use of the clinicalconcept. The method further includes, for each of the clinical concepts,determining a truth status for each use of the clinical concept in theelectronic medical record, wherein the truth status indicates whetherthe clinical concept was expressed positively, negatively, ambiguously,or unknown. The method further includes, for each of the clinicalconcepts, determining a clinical-usage context for each use of theclinical concept in the electronic medical record, wherein theclinical-usage context describes how the clinical concept was used inthe electronic medical record. The method also includes, for each of theclinical concepts, determining a specificity factor for each use of theclinical concept in the electronic medical record based on the degree ofspecificity, precision or narrowness of scope of the concept, as derivedfrom the concept's position in a clinical ontology, or other referenceinformation. The method further includes assigning a clinical-importancescore to each use of the clinical concept in the electronic medicalrecord based on a the patient-subject status, the truth status, theclinical-usage context, and the specificity factor and storing theclinical-importance score associated with each use of the clinicalconcept in each document within the electronic medical record in a datastore.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the attached drawing figures, wherein:

FIG. 1 is a block diagram depicting an exemplary computing environmentsuitable for use in implementing embodiments of the present invention;

FIG. 2 is a block diagram depicting an exemplary computing architecturesuitable for searching an electronic medical record for components thatare responsive to a search query, in accordance with an embodiment ofthe present invention;

FIG. 3 is a flow diagram showing a method of preparing electronicmedical records for electronic searching, in accordance with anembodiment of the present invention;

FIG. 4 is a flow diagram showing a method of finding information inelectronic medical record, in accordance with an embodiment of thepresent invention;

FIG. 5 is a flow diagram showing a method of searching an electronicmedical record for a selected clinical concept, in accordance with anembodiment of the present invention;

FIG. 6 is an illustrative screen display of an interface to receive asearch query, in accordance with an exemplary embodiment of the presentinvention;

FIG. 7 is an illustrative screen display of search results that areresponsive to a submitted clinical concept, in accordance with anexemplary embodiment of the present invention;

FIG. 8 illustrates the identification of clinical concepts in a documentthat is part of the electronic medical record, in accordance with anexemplary embodiment of the present invention; and

FIG. 9 illustrates indexing clinical concepts and related information inan index, in accordance with an exemplary embodiment of the presentinvention.

DETAILED DESCRIPTION

Embodiments of the present invention allow a user to search forinformation in an electronic medical record (“EMR”). An EMR is acollection of information describing the medical history of a patient.The EMR may be managed by a variety of sources including a clinicalfacility, such as a hospital, and the patient. In one embodiment, theEMR is personal health record. The EMR for a single patient may containcombinations of database entries and electronic documents that arerelated to the patient's medical history. The database entries may becreated by filling out an electronic form presented in a user interface.The documents and database entries may include encoded data thatdescribes a portion of a patient's medical history. For example, adiagnosis for diabetes may be codified as “D234539A293” and recorded ina document or database entries. EMRs for groups of patients may becollected in a single data store.

Embodiments of the present invention allow a user to submit a searchquery through an interface and return search results that are responsiveto the search query. As will be described in more detail subsequently,the search results may be ordered according to a query-responsivenessscore so that the most important matching components of the EMR can bequickly located at the top of the result list. Search results maypresent components of the EMR at any level of granularity. A componentof the EMR may be any text within the EMR including a document withinthe EMR or a section of text within a document (e.g., a paragraph,document section) in the EMR, or any structured and/or codified elementof information contained within the EMR. For example, components may bedocuments within the EMR, a word within the EMR, a sentence within theEMR, a single use of a clinical concept within the EMR, or a componentof a document within the EMR. Different embodiments evaluate componentsof the EMR and present search results based on those components atdifferent levels of granularity. The analysis used to determine theresponsiveness of a search result, which is described hereafter, may beperformed at whatever level of granularity the search results arepresented. Throughout this disclosure, the level of granularity willmost commonly be described as a document in the EMR or a component ofthe EMR, but embodiments of the present invention are not intended to belimited to these descriptions. As will be pointed out subsequently, someof the factors used to determine the responsiveness of a search resultmay not be used when the search results are at a very low level ofgranularity, such as a single word or a single use of a clinicalconcept. In one embodiment, the search results are matched based onclinical concepts in the query and clinical concepts in components ofthe EMR. A clinical concept is an aspect related to a person's health. Aclinical concept describes any aspect of a person's health condition, orany object, action, attribute or idea that is related to a healthcondition. Examples include: diseases; symptoms; clinical observationsand findings; diagnostic tests; diagnostic or therapeutic procedures;organisms, substances, devices or products related to health conditions;anatomic structures including genomic; phenotypic expression; behavior,family and social context related to health conditions; risk factors andoutcomes; facilities and care providers. For example, heart disease anda heart attack are examples of clinical concepts.

The search results are ordered according to a query-responsiveness scorethat is calculated for each matching search result (e.g. document,document portion). In one embodiment, the query-responsiveness score iscalculated by combining a set of clinical-importance scores with a setof boost factors. The clinical-importance score measures how importantthe clinical concepts used within each specific component of the EMRare, apart from a query. In one embodiment, the clinical-importancescore is generated for each use of a clinical concept within the EMR inadvance and stored in an index.

There are at least two categories of boost factors that may be combinedwith the clinical-importance score to calculate the query-responsivenessscore. Document-boost factors measure the responsiveness of asearch-result (e.g., a document, a component of a document, a componentof the EMR) without considering the query. Query-boost factors measurethe document's responsiveness to the query using information related tothe query, such as the role of the user submitting the query. The boostfactors are assigned to produce a good fit between the query and thepotential search result. One or more boost factors may be combined withthe set of clinical-importance scores to generate a query-responsivenessscore for the component of the EMR.

Having briefly described embodiments of the present invention, anexemplary operating environment suitable for use in implementingembodiments of the present invention is described below. Some of thewording and form of description is done to meet applicable statutoryrequirements. Although the terms “step” and/or “block” or “module” etc.might be used herein to connote different components of methods orsystems employed, the terms should not be interpreted as implying anyparticular order among or between various steps herein disclosed unlessand except when the order of individual steps is explicitly described.

Referring to the drawings in general, and initially to FIG. 1 inparticular, an exemplary computing system environment, for instance, amedical information computing system, on which embodiments of thepresent invention may be implemented is illustrated and designatedgenerally as reference numeral 20. It will be understood and appreciatedby those of ordinary skill in the art that the illustrated medicalinformation computing system environment 20 is merely an example of onesuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the medical information computing system environment 20be interpreted as having any dependency or requirement relating to anysingle component or combination of components illustrated therein.

The present invention may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the presentinvention include, by way of example only, personal computers, servercomputers, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, cellular telephones, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove-mentioned systems or devices, and the like.

The present invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks or implement particular abstract datatypes. The present invention may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inlocal and/or remote computer-storage media including, by way of exampleonly, memory storage devices.

With continued reference to FIG. 1, the exemplary medical informationcomputing system environment 20 includes a general purpose computingdevice in the form of a control server 22. Components of the controlserver 22 may include, without limitation, a processing unit, internalsystem memory, and a suitable system bus for coupling various systemcomponents, including database cluster 24, with the control server 22.The system bus may be any of several types of bus structures, includinga memory bus or memory controller, a peripheral bus, and a local bus,using any of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronic Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

The control server 22 typically includes therein, or has access to, avariety of computer-readable media, for instance, database cluster 24.Computer-readable media can be any available media that may be accessedby control server 22, and includes volatile and nonvolatile media, aswell as removable and non-removable media. By way of example, and notlimitation, computer-readable media may include computer-storage mediaand communication media. Computer-storage media may include, withoutlimitation, volatile and nonvolatile media, as well as removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. In this regard, computer-storage mediamay include, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVDs) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage, or other magnetic storage device, or any other medium which canbe used to store the desired information and which may be accessed bythe control server 22. Communication media typically embodies computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. As usedherein, the term “modulated data signal” refers to a signal that has oneor more of its attributes set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media. Combinations of any of the abovealso may be included within the scope of computer-readable media.

The computer-storage media discussed above and illustrated in FIG. 1,including database cluster 24, provide storage of computer readableinstructions, data structures, program modules, and other data for thecontrol server 22.

The control server 22 may operate in a computer network 26 using logicalconnections to one or more remote computers 28. Remote computers 28 maybe located at a variety of locations in a medical or researchenvironment, for example, but not limited to, clinical laboratories(e.g., molecular diagnostic laboratories), hospitals and other inpatientsettings, veterinary environments, ambulatory settings, medical billingand financial offices, hospital administration settings, home healthcare environments, and clinicians' offices and the clinician's home orthe patient's own home or over the Internet. Clinicians may include, butare not limited to, a treating physician or physicians, specialists suchas surgeons, radiologists, cardiologists, and oncologists, emergencymedical technicians, physicians' assistants, nurse practitioners,nurses, nurses' aides, pharmacists, dieticians, microbiologists,laboratory experts, laboratory technologists, genetic counselors,researchers, veterinarians, students, and the like. The remote computers28 may also be physically located in non-traditional medical careenvironments so that the entire health care community may be capable ofintegration on the network. The remote computers 28 may be personalcomputers, servers, routers, network PCs, peer devices, other commonnetwork nodes, or the like, and may include some or all of the elementsdescribed above in relation to the control server 22. The devices can bepersonal digital assistants or other like devices.

Exemplary computer networks 26 may include, without limitation, localarea networks (LANs) and/or wide area networks (WANs). Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet. When utilized in a WAN networkingenvironment, the control server 22 may include a modem or other meansfor establishing communications over the WAN, such as the Internet. In anetworked environment, program modules or portions thereof may be storedin the control server 22, in the database cluster 24, or on any of theremote computers 28. For example, and not by way of limitation, variousapplication programs may reside on the memory associated with any one ormore of the remote computers 28. It will be appreciated by those ofordinary skill in the art that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers (e.g., control server 22 and remote computers 28) may beutilized.

In operation, a user may enter commands and information into the controlserver 22 or convey the commands and information to the control server22 via one or more of the remote computers 28 through input devices,such as a keyboard, a pointing device (commonly referred to as a mouse),a trackball, or a touch pad. Other input devices may include, withoutlimitation, microphones, satellite dishes, scanners, or the like.Commands and information may also be sent directly from a remotehealthcare device to the control server 22. In addition to a monitor,the control server 22 and/or remote computers 28 may include otherperipheral output devices, such as speakers and a printer.

Many other internal components of the control server 22 and the remotecomputers 28 are not shown because such components and theirinterconnection are well known. Accordingly, additional detailsconcerning the internal construction of the control server 22 and theremote computers 28 are not further disclosed herein.

Although methods and systems of embodiments of the present invention aredescribed as being implemented in a WINDOWS or LINUX operating system,operating in conjunction with an Internet-based delivery system, one ofordinary skill in the art will recognize that the described methods andsystems can be implemented in any system supporting the search ofelectronic medical records. As contemplated by the language above, themethods and systems of embodiments of the present invention may also beimplemented on a stand-alone desktop, personal computer, cellular phone,smart phone, PDA, or any other computing device used in a healthcareenvironment or any of a number of other locations.

Turning now to FIG. 2, a block diagram is illustrated that shows anexemplary computing-system architecture 200 suitable for searching anelectronic medical record for components that are responsive to a searchquery, in accordance with an embodiment of the present invention. Thecomputing-system architecture 200 shown in FIG. 2 is merely an exampleof one suitable computing system and is not intended to suggest anylimitation as to the scope of the use or functionality of the presentinvention. The computing system architecture 200 should not beinterpreted as having any dependency or requirement related to anysingle component/module or combination of component/modules illustratedtherein.

The components in computing-system architecture 200 work together toanalyze and index an EMR and analyze a search query to provide a userwith the search results that are most responsive the user's searchquery. Computing-system architecture 200 includes clinical-conceptcomponent 210, clinical-usage component 215, frequency component 220,specificity-factor component 225, truth component 230, patient-subjectcomponent 235, clinical-importance component 240, document-scoringcomponent 245, search-engine component 250, search-interface component255, query-expansion component 260, query-scoring component 265, EMRdata store 270, search-index data store 275, and clinical-concept datastore 280. Computing-system architecture 200 may operate in a singlecomputing device, such as control server 22 shown in FIG. 1. In thealternative, computing-system architecture 200 may operate in adistributed computing environment that includes multiple computingdevices coupled with one another via one or more networks. Such networksmay include, without limitation, one or more local area networks (LANs)and/or one or more wide area networks (WANs). Such network environmentsare commonplace in offices, enterprise/wide computer networks,intranets, and the Internet. Accordingly, the network, or combination ofnetworks, is not further described herein.

Clinical-concept component 210 may identify clinical concepts within asearch query and components of an EMR. The clinical-concept component210 may interact with the clinical-concept data store 280 in order toidentify clinical components. The clinical-concept data store 280contains at least one clinical-concept nomenclature and may containseveral. A clinical-concept nomenclature is a vocabulary of medicalterminology. A vocabulary contains terms and expressions related to aspecific domain. Clinical nomenclatures may describe conditions,symptoms, diagnostic procedures, organisms, substances, treatments andother clinically relevant concepts. The data store 280 may also containlocal, non-clinical concepts such as facilities, locations, careproviders. A clinical-concept ontology may contain information about theattributes and relations of the terms in a clinical-conceptnomenclature. At the simplest level, the clinical-concept ontologyorganizes terms around clinical concepts, and relates “synonyms” orterms that describe the same concept. For example, the terms “heartattack” and “myocardial infarction” describe the same clinical concept,and can be related as synonyms. The ontology can also contain morecomplex relationships. Specificity is expressed as a hierarchy of “is-a”relationships. For example, the concept described as “left main coronaryartery disease” can be related to the more general concept of “heartdisease,” of which it “is a” specific form. Additional relations dependon the nature of the clinical-concept ontology. They can includerelations that link disease with similar causes, a disease to a symptomthat it causes, a medication to the disease it treats, and many more. Inone embodiment, SNOMED CT (Systematized Nomenclature ofMedicine-Clinical Terms) is used as the clinical-concept ontology.SNOMED CT is a systematically organized, computer-processable collectionof medical terminology.

Clinical-concept component 210 may identify each of the clinicalconcepts within an electronic medical record. The clinical concepts maybe identified by performing a textual analysis on the EMR and matchingphrases to clinical concepts within a clinical-concept ontology.Clinical concepts may also be identified by mapping codified data in theEMR to the clinical-concept ontology. For example, the codified datadescribing a diagnosis may be mapped to the clinical concept describingthe diagnosis. Once identified, the clinical concepts within the EMR maybe stored in an index used for searching the electronic medical record.Additional information describing the use and context of the clinicalconcept within the EMR may also be indexed. The additional informationmay be provided by other components. The function of those components isdescribed subsequently. In one embodiment, each use of a clinicalconcept is indexed according to the document or component of the EMR inwhich the clinical concept is used.

The clinical-concept component 210 may also identify clinical conceptswithin the search query using a similar methodology. The clinicalconcepts identified in the search query may be communicated to thesearch-engine component 250. Clinical-concept component 210 may expand aspecific clinical-concept found in the search query by traversing theclinical-concept ontology to find more general, more specific, and/orsibling clinical concepts. The expanded set of clinical concepts may beused to expand the search criteria and/or to expand the informationindexed that describes a particular document or component in the EMR.For example, the primary clinical concept identified in the search queryand expanded clinical concepts may be used to identify matching clinicalconcepts in an index. Additionally, expanded clinical concepts may beindexed along with the primary clinical concept used in the documents orcomponents in the EMR.

Clinical-usage component 215 performs part of an analysis that is usedto determine the importance of a particular use of a clinical conceptwithin an EMR. The clinical-usage component 215 ascertains the contextor role in which a clinical concept is used the EMR. The particular useof a clinical concept may be assigned a pre-defined clinical-contextcategory. The clinical-usage context may be determined by ascertainingin what part of a document the clinical concept is used, or how theclinical concept is used in certain sentences. Examples ofclinical-usage contexts include: the presenting complaint, patienthistory, family history, review of systems, physical exam, prescription,order, lab result, vital sign, diagnosis, procedure, and others. Thislist of clinical-usage contexts is not meant to be exhaustive.

The clinical-usage component 215 may perform natural language processingand grammatical analysis to determine the clinical-usage context.Document metadata may also be analyzed to determine what role a portionof a document or a sentence in a document plays in the electronicmedical record. Other markers and headings included within the EMR orwithin documents that are within the EMR may be used to help identifythe clinical-usage context. The clinical-usage context may be stored inan index, such as the index in search-index data store 275, along withthe use of the associated clinical concept. The clinical-usage contextmay be used as a factor in assigning a clinical-importance score to aparticular use of a clinical concept. The determination of aclinical-importance score is described in more detail subsequently.

The frequency component 220 analyzes the frequency with which a clinicalconcept occurs within an electronic medical record. The frequencycomponent 220 may assign a frequency score to each use of a clinicalconcept. In one embodiment, the frequency score is the result of acalculation. For example, the number of uses of a particular-clinicalconcept in an EMR divided by the number of uses of all clinical conceptsin the EMR. Clinical concepts that occur alone in a document may be lessimportant than clinical concepts that occur multiple times in adocument. In other words, multiple appearances of the same or relatedclinical concepts within a document may indicate that the clinicalconcept is a main subject of the document. A clinical concept thatoccurs rarely across multiple electronic medical records may have moreimportance than common clinical concepts. In one embodiment, twofrequencies scores are calculated. The first frequency score is based onoccurrences of the clinical concept within a document in the EMR and thesecond score is based on the number of occurrences of the clinicalconcept within the entire EMR or across multiple EMRs. The actualfrequency score or a frequency rank may be stored in an index with theassociated the use of the clinical concept. The frequency score may beused to determine a clinical-importance score for the use of theclinical concept.

The specificity component 225 determines the specificity factor of aclinical concept based on the degree of specificity, precision ornarrowness of scope of the concept, as derived from the concept'sposition in a clinical ontology, or other reference information. Thespecificity factor represents the precision, or degree of detail of aclinical concept. For example, “heart disease” is a general, or a fairlynon-precise concept, whereas “right coronary artery occlusion” (a formof heart disease) is a precise concept. The specificity factor is apowerful tool for ranking and evaluation because precise clinicalconcepts tend to be more clinically interesting and significant, andalso because the documents that describe concepts in very preciselanguage are usually the documents that contain the most clinicallyinteresting information about that concept. The specificity factor maybe derived from a combination of the concept relations in aclinical-concept ontology, and additional content and algorithms. Thespecificity factor may be expressed as a numeric level within ahierarchy. Alternatively, the specificity factor may be expressed as acategory. For example, the most general clinical concepts may bedesignated with a specificity factor of “1” or as “low” specificity.More precise concepts could be grouped as medium or high. Thespecificity factor may be indexed in association with each use of theclinical concept. The specificity factor may be used as an additionalfactor to determine a clinical-importance score for the clinicalconcept.

The truth component 230 performs a grammatical analysis to assign atruth status to a use of a clinical concept. The truth status indicateswhether the use of the clinical concept is positive, negative,ambiguous, or unknown. For example, the phrase “the patient complainedof chest pain” would create a positive truth status for to the clinicalconcept “chest pain.” On the other hand, the phrase “the patient denieschest pain” would warrant a negative truth status for “chest pain”because the patient said he had not had chest pain. The truth status maybe stored in an index in association with the particular use of theclinical concept. The truth status may be used to determine animportance score for a particular use of the clinical concept. Anegative truth status may make a use of a clinical concept lessimportant. A positive truth status may increase the importance scorecalculated for the particular use of the clinical concept.

The patient-subject component 235 determines the patient-subject statusof a use of a clinical concept. The patient-subject status indicateswhether the patient is the subject being described by the clinicalconcept. For example, a medical history indicating the patient's fatherdied of cancer would not have the patient as the subject of the clinicalconcept “cancer” and the patient-subject status would be false. On theother hand, the patient is the subject of the clinical concept “chestpain” in a reference to the patient complaining of chest pain in apresenting complaint. In this case, the patient-subject status would betrue. The clinical subject may be stored in an index in association withthe particular use of the clinical concept. The patient-subject statusmay be used as an additional factor to determine a clinical-importancescore for the use of the clinical concept.

The clinical-importance component 240 assigns a clinical-importancescore to each use of a clinical concept within the electronic medicalrecord. A use of a clinical concept is a single occurrence of theclinical concept within the EMR. A single clinical concept may be usedmultiple times within a single document within the EMR as well as withinother documents in the EMR. Each use of a clinical concept may beassigned a clinical-importance score. The purpose of theclinical-importance score is to quantify how important a particular useof a clinical concept is likely to be to a person searching forcomponents of the EMR in which the clinical concept is used. The moreimportant the use, the more likely the document containing the use isinteresting to the person submitting the search query. However, theclinical-importance score is calculated based on the use of a clinicalconcept within the EMR and without reference to any specific searchquery.

The clinical-importance score may be calculated based on severalfactors. Different embodiments of the present invention combinedifferent factors to calculate each clinical-importance score. It maynot be necessary to use every factor explained herein to calculate theclinical-importance score. Further, different weights may be given todifferent factors when calculating the clinical-importance score.Embodiments of the present invention are not limited to the specificexamples given. In one embodiment, the clinical-importance score iscalculated based on the clinical-usage context, the truth status, thepatient-subject status, the specificity factor, and the frequencyassociated with the particular use of the clinical concept in thisdocument compared to other matching documents in the patient's EMR. Inanother embodiment, the clinical-importance score is calculated based onthe truth status, the patient-subject status, and the clinical-usagecontext. In another embodiment, the clinical-importance score iscalculated based only on the clinical-usage context.

As stated previously, different weighting may be given to the differentfactors used to calculate the clinical-importance score. Regardless ofthe weight given to a factor in a particular embodiment, in general, thefactors may increase or decrease the ultimate score as explainedsubsequently. When the patient is the subject of the use of the clinicalconcept, the importance score may be increased. A negative truth statusfor a use of a clinical concept may lower the importance score. Theimportance score may be raised if the clinical-usage context is in acategory that is authoritative and related to the patient. For example,if the clinical-usage context is the presenting complaint or the patienthistory, the importance score could be raised more than if theclinical-usage context is a patient's family history, since the familyhistory is less directly related to the patient. Different categories ofclinical-usage context may be given different values to plug into thecalculation of the clinical-importance score. In general, the moreclosely the clinical concept is related to the patient and the moreauthoritative, the higher the importance score will be. A relativelyhigh frequency of occurrence in a document within the EMR compared toother documents may increase the importance score because the clinicalconcept is more likely to be the subject of the document, and thus, moreimportant than a clinical concept that may be mentioned tangentially orthat is used indiscriminately in many documents.

Document-analysis component 245 gathers attributes describing componentsof the electronic medical record. In one embodiment, the components ofthe EMR are analyzed at the document level of granularity. A componentof the EMR may contain multiple uses of one or more clinical concepts.The document or component attributes may be stored in the index and usedto calculate document-boost factors that increase a responsiveness rankof a matching component within the search results.

In addition to collecting document attributes, the document-analysiscomponent 245 may also assigns each document one or more primary-focusdomains and one or more generic-document domains. For example, theprimary-focus domain may be “physician” for a particular documentauthored by a physician. Other examples of primary-focus domains include“nurse-focused” and “social-worker-focused.” The primary-focus domainand the generic-domain may be used for calculating boost factors. Thegeneric-document domains may include a designation that the document isa clinical document, lab result, vital sign, problem, order, or otherclinical event. The primary-focus domain and generic-documents domainsmay be stored in an index.

Many different attributes and collections of attributes may be used tocalculate document-boost factors. Document-boost factors include asource-boost factor, a class-boost factor, and a document-type-boostfactor. A source-boost factor may be calculated based on the source of adocument. The source-boost factor may be increased or decreasedaccording to the importance of the source of the document. For example,the source-boost factor may be increased if the document is a primarysource, such as a discrete lab result in contrast to a secondary source,such an end-of-the-day summary that repeats the lab result. In additionto the source-boost factor, a class-boost factor may be used. Theclass-boost factor is based on the importance of a document asdetermined by the importance of the class of which the document is apart. The class-boost factor may be increased if the document class is a“clinician-authored document.” In contrast, the class-boost factor maybe lowered if the document class is “a procedure note,” or is authoredby a non-clinician. In addition to the class-boost factor andsource-boost factor, the document type may be considered whencalculating a document-type-boost factor. For example, thedocument-type-boost factor may be increased if the document is adischarge summary or transfer summary. Both the discharge summary andtransfer summary tend to be more important because they containauthoritative summaries of a patient's progress. Similarly, an admissionnote may be more important because it may define the cause of a newepisode. Daily progress notes, medical student notes, chart abstractornotes, and other notes may be less important and would tend to decreasethe boost factor.

The domains may also be incorporated into document-boost factors orquery-boost factors that are used as part of the calculation of thequery-responsiveness score. For example, the domains may be used incombination with information related to the query to calculate arole-boost factor. The role-boost factor is based on matching a searchquery submitted by a particular category of clinician (e.g., nurse,doctor, social worker) to a document associated with the same categoryof clinician. For example, if the search query is submitted by a nurse,then the document's query-responsiveness score may be increased, througha boost factor, if the primary-focus domain is nurse. Similarly, if asearch query is entered by a social worker, then thequery-responsiveness score may be increased if the primary focus of thedocument is social worker.

Search-engine component 250 receives a search query and retrievesdocuments or components of documents from the electronic medical recordthat are responsive to the search query. A document or component of adocument is responsive to the search query when a portion of thedocument or component of a document matches the search query or anexpansion of the search query. Search-engine component 250 may use thesearch index in search-index data store 275 to find search results. Thesearch results may be presented in an order intended to present the mostresponsive results first. The most responsive results may be determinedusing a query-responsiveness score (to be discussed subsequently). Thesearch-engine component 250 may present search results that describecomponent of an EMR, a document in an EMR, or a component of a documentin the EMR. A component of a document may be one or more words withinthe document.

Search-interface component 255 presents a graphical user interface to auser for receiving search criteria and presenting search results. Anexample of a graphical user interface is shown in FIGS. 6 and 7. Thesefigures will be described in more detail subsequently. In addition, thesearch-interface component 255 may also present filter options.

Query-expansion component 260 expands the search query to create aplurality of expanded search terms. The plurality of expanded searchterms are used by the search-engine component 250 to find and rankadditional search results. The degree of expansion performed by thequery-expansion component 260 may differ depending on the search modeselected. For example, in a text only search mode, the query-expansioncomponent 260 may be limited to only stemming each word within thesearch query. Stemming a word reduces the word to its root. For example,the stem of “dogs” is “dog,” and the stem of “changing” is “change.”Query-expansion component 260 may, in conjunction with other components,expand terms in the search query. For example, the plurality of expandedsearch terms may include medical synonyms of words submitted in thesearch query. In addition, clinical concepts identified within thesearch query may be expanded to include sibling, child, and/or relatedclinical concepts from the clinical-concept ontology. Thequery-expansion component 260 may transmit the search terms and expandedsearch terms to the search-engine component 250.

Query-scoring component 265 assigns a query-responsiveness score to eachsearch result returned in response to a search query. As describedpreviously, a search result may be any component of the EMR, including adocument or a part of a document. For the sake of simplicity, thecalculation of a query-responsiveness score will be described using adocument as a search result. However, a query-responsiveness score maybe calculated for any portion of the EMR for which the necessaryinformation is available. The search results may be received from thesearch-engine component 250.

The query-responsiveness score for an individual document matching thequery may be calculated by combining the clinical-importance scoresassigned to each use of a clinical concept in the individual documentwith one or more document-boost factors and query-boost factors. Thequery-scoring component 265 may calculate one or more boost factorsincluding the closeness-boost factor.

A closeness-boost factor may be calculated for each matching combinationof clinical concepts when the primary clinical concept in the query isexpanded. A matching combination of clinical concepts includes aclinical concept related to the query and a use of the same clinicalconcept in the particular document. The clinical concept related to thequery may be the primary clinical concept and/or expanded clinicalconcepts. The closeness-boost factor may be used to increase thequery-responsiveness score for combinations including the primaryclinical concept and expanded clinical concepts that are close to theprimary clinical concept. Since a user's query may be exploded into aset of synonyms, child, and related concepts, in one embodiment, thecloseness-boost factors are assigned to combinations with the primaryclinical concept and to combinations with the expanded clinical conceptssuch that combinations with the primary clinical concept have the mostimportance, and combinations with the more distant ‘related’ conceptshave less importance. In general, the closer on the clinical-conceptontology the expanded clinical concept is to the primary (e.g. original)clinical concept, the higher the closeness-boost factor. In contrast,the further away on the clinical-concept ontology that the expandedclinical concept is, the lower the boost factor. The closeness-boostfactors are combined with the clinical-importance scores to determinethe query-responsiveness score of the document or component. Forexample, a document including a clinical concept with a highclinical-importance score matching to an expanded clinical concept thatis a sibling of (i.e., close to) the primary clinical-concept wouldreceive a higher query-responsiveness score than when the same clinicalconcept is combined with an expanded clinical concept several layersaway from the primary clinical concept. A boost factor based on thecloseness of the clinical concept from the query is only used when thequery is expanded. Other boost factors may be used to calculate into thequery-responsiveness score when the query is not expanded or incombination with the closeness-boost factor given combinations of theprimary-clinical concept and closely related expanded-clinical concepts.

In addition to the closeness-boost factor, additional boost factors maybe used based on other document and/or query attributes. For example, arole-boost factor based on the role to the person submitting the queryand the role of the person associated with a component of the EMR may beused. The role-boost factor may be calculated by the query-scoringcomponent 265. The role-boost factor may be increased for a documentwith a primary-focus domain of “nurse” when the query is submitted by anurse. Similarly, the class-boost factor used. As described previously,the class-boost is based on the importance of a document-class. Forexample, the class-boost factor may be higher if the document class is“physician authored” and lower if the document class is “medical-studentauthored.” In another embodiment, a boost factor is time weighted. Thetime-weighted-boost factor increases for documents that are more recent.In yet another embodiment, the clinical-facility-boost factor increasesif the document was generated in the same clinical facility as the onefrom which the query is submitted. Additional boost factors may becalculated based on one of more of the previously described attributes.All of the previously described boost factors may be combined, but notall boost factors need be used to calculate a query-responsivenessscore. A query-responsiveness score may be calculated using just one, ornone, of the previously described boost factors.

The Filter component 267 allows the user to filter the search resultsaccording to suggested or submitted criteria. For example, the filtercomponent may provide an interface allows the user to filter searchresults according to date, document class, clinical facility, and thedocument's primary focus. In one embodiment, the filter component 267suggests a filter criteria for the user to select along with anindication of how many of the search results match the filteredcriteria. For example, the interface could indicate that 20 searchresults are in the document class “physician authored.” In oneembodiment, the search results may be filtered by clinical conceptsfound within the search results. Related clinical concepts may beaggregated into a general filter option that would present searchresults that include any of the related clinical concepts. The filteroptions could be presented with the clinical concepts having the highestaggregation of clinical-importance scores. These filter examples are notmeant to be exhaustive, other filters based on factors store in theindex are within the scope of this disclosure.

The Electronic medical record data store 270 contains the electronicmedical records for one or more patients. An EMR is a collection ofinformation describing the medical history of a patient. In addition,the EMR data store 270 may include electronic medical records from oneor more clinical facilities. The EMR data store 270 may be accessed byother components within computing-system architecture 200.

Search-index data store 275 includes a search index which stores thewords and the clinical concepts extracted from the patient's documentsas described above. The search index may be isolated on a per patient orper clinical facility basis. In other embodiments, multiple patients andeven multiple EMRs may be searched concurrently.

Clinical concept data store 280 contains one or more clinical-conceptontologies as described previously. In one embodiment, theclinical-concept ontology is based on a combination of SNOMED CT (torepresent clinical conditions, symptoms, therapy, organisms, etc.) andRxNorm (to represent medications). Embodiments of the present inventionare not limited to using SNOMED CT. Other hierarchies of medicalterminology may be used.

Turning now to FIG. 3, a method 300 of preparing electronic medicalrecords for electronic searching is provided, in accordance with anembodiment of the present invention. At step 310, an electronic medicalrecord that includes information describing at least a portion of amedical history associated with a patient is received. As describedpreviously, an electronic medical record includes information thatdescribes a patient's medical conditions, treatments received, and othermedical information for the patient. The EMR may be recorded byclinicians, the patient, or others. Electronic medical records may begenerated by a single clinical facility or by multiple clinicalfacilities. The electronic medical record may be stored in a data storethat is accessible to a single clinical facility or to multiple clinicalfacilities.

In one embodiment, any information in the electronic medical record thatis not already formatted as an displayable document is converted into anew displayable document. The electronic medical record may containstructured and/or codified data including database entries. In oneembodiment of the present invention, the database entries are convertedinto electronic documents. The converted electronic documents includeentries from the database fields and descriptions of the databasefields. Thus, once non-document portions are formatted, an electronicmedical record may consist of a plurality of electronic documents thatare displayable by a search engine as a search result. In oneembodiment, the newly created electronic documents may be used ascomponent of the EMR to associate with boost factors.

At step 320, clinical concepts in the electronic medical record areidentified. A clinical concept describes any aspect of a person's healthcondition, or any object, action, attribute or idea that is related to ahealth condition. Examples include: diseases; symptoms; clinicalobservations and findings; diagnostic tests; diagnostic or therapeuticprocedures; organisms, substances, devices or products related to healthconditions; anatomic structures including genomic; phenotypicexpression; behavior, family and social context related to healthconditions; risk factors and outcomes; facilities and care providers.For example, heart disease and a heart attack are examples of clinicalconcepts.

The identification of clinical concepts in a document that is part of anelectronic medical record is illustrated in FIG. 8. For the sake ofclarity, only a few of the clinical concepts and significant words areshown. The electronic document 800 in FIG. 8 is a summary of a patient'svisit to a doctor's office. The electronic document 800 includes adocument type 810. The document type 810 field is a description of whatcategory the electronic document is in. In this case, the document type810 is an office visit. The document date 812 is Oct. 4, 2005. Thedocument title 814 is “Mr. X., pepperoni pizza with hot peppers.” Thedocument author 816 is “T. Jones, M.D.” The document source 818 is“River Heights Medical Association.” These document attributes may beassociated with each word or use of a clinical concept within thedocument. These document attributes may also be included as documentmetadata. Factors, such as the clinical-usage context may be determinedusing these factors.

The words in electronic document 800 that are in blocks may be clinicalconcepts. The blocked words include “peppers” 820, “heartburn” 822,“peppers” 824, “heartburn” 825, “heart attack” 826, “G.I. ulcer” 828,“abdominal hernia repair” 830, and “heart disease” 832. These words maybe selected based on analysis that looks for medical terms, keywords andother words/terms of interest.

Returning now to FIG. 3, at step 330, a patient-subject status for eachuse of a clinical concept of the electronic medical record isdetermined. The patient-subject status indicates whether the patient isthe subject of the clinical concept. For example, the patient is theobject of the sentence “he has never been diagnosed with a G.I. ulcer”in electronic document 800. The patient-subject status may be determinedusing a grammatical analysis of text in which the clinical conceptoccurs. Each instance of a clinical concept may be associated with adifferent patient-subject status. The patient-subject status may berecorded as true\false. True indicates that the patient is the subjectof the clinical concept. In another embodiment, the patient-subjectstatus may be recorded with the name of the subject associated with theclinical concept. For example, the patient-subject status may berecorded as “patient's father.”

At step 340, a truth status of the particular-clinical concept isdetermined. The truth status indicates whether the clinical concept wasexpressed positively, negatively, ambiguously, or unknown. For example,referring to electronic document 800 “the worst heart burn I ever had,so bad I thought I was having a heart attack,” refers positively toheartburn and ambiguously to heart attack. In this example, the truthstatus could be recorded as positive for both heartburn and ambiguousfor heart attack.

At step 350, a clinical-usage context of each clinical concept isdetermined. The clinical-usage context describes how the clinicalconcept was used in electronic medical record. The clinical-usagecontext may be determined based on the section of the electronicdocument in which the use of the clinical concepts occurs or by analysisof the grammar of the sentence in which the concept is used. Theclinical-usage context may also be determined based on the type ofdocument in which the use of the clinical concept occurs. For example,“heartburn” 825 in electronic document 800 is in the clinical-usagecontext of the presenting complaint. “Abdominal hernia repair” 830 inelectronic document 800 is in the clinical-usage context of a patientmedical history. Each clinical concept may be categorized into one ormore of a predefined group of clinical-context categories. Examples ofclinical-usage contexts include a presenting complaint section, apatient history, a family history, a review of systems section, aphysical exam record, a prescription, order, a lab result, a vital sign,a diagnosis, and a procedure record.

At step 360, a specificity factor is determined for theparticular-clinical concept based on the degree of specificity,precision or narrowness of scope of the concept, as derived from theconcept's position in a clinical ontology, or other referenceinformation. As described previously, the clinical-concept ontologydescribes relationships between clinical concepts. The specificityfactor could be recorded as a group category, such as high medium, orlow. The specificity factor could also be recorded as a level in theclinical-concept ontology.

In one embodiment, a document-importance factor is determined for theparticular use of the clinical concept. The document importance is therelevance of the particular-clinical concept to the main subject of thedocument. This is determined by clustering the clinical concepts in thedocument into clinical categories. In the case of diseases and symptoms,the cluster of related concepts would correspond tobody-system-condition categories. The frequency of references to eachcategory is evaluated to determine the “subject(s)” or areas of focus ofthe document. Weighting is also applied based on the clinical usage ofthe concepts. For example, consider two documents that each contain oneuse of the concept “heart disease.” One document also contains manyconcepts related to heart disease, such as the diagnostic test“echocardiogram,” the medication therapy “statin,” and the clinicalfinding “S-T segment depression.” In addition, the therapeuticintervention “cardiac catheterization” is contained in the significantclinical usage of “assessment and plan.” In the second document, “heartdisease” is also mentioned, but without any closely related concepts.The “document importance” of the concept “heart disease” is high in thefirst instance, low in the second.

At step 370, a clinical-importance score is computed and assigned toeach clinical concept. The clinical-importance score may be determinedbased on the patient-subject status of the particular-clinical concept,the truth status of the particular-clinical concept, the clinical-usagecontext of the particular-clinical concept, and the specificity factorof the particular-clinical concept. The various factors may be combinedand given different weights to arrive at the clinical-important score.In other embodiments of the present invention, additional factors areused to calculate the clinical-important score. For example, asdescribed in FIG. 2, the frequency of a particular-clinical conceptwithin the electronic medical record or a plurality of electronicmedical records may be used.

At step 380, the clinical-importance score associated with each clinicalconcept is stored in the document index. The index may store aclinical-importance score for each use of a clinical concept inassociation with the document in which each clinical concept is used. Inanother embodiment, the index may store a clinical-importance score foreach use of a clinical concept in association with a component of theEMR in which each clinical concept is used. The storage ofclinical-important scores in a searchable index is illustrated by FIG.9. The index 900 in FIG. 9 includes text 910 from electronic document800 shown in FIG. 8. The first row of text 910 includes “heartburn” 822,“heartburn” 825, “heart attack” 826, “heart attack” 828, “abdominalhernia repair” 830, and “G.I. ulcer” 832. Words such as “pepper” 820 arenot included in the concept index because they are not associated withclinical concepts, thought they may be stored in an ordinary “word”index so that they can help refine searches for subjects that aren'tclinical concepts. The next row lists the clinical concept 912 withwhich the word is associated. For example the word “heartburn” 822 isassociated with clinical concept “heartburn” 913. Similarly “heartburn”825 is associated with “heartburn” 914. Both “heartburn” 913 and“heartburn” 914 have the same SNOMED CT code. As described previously,the SNOMED CT is a hierarchy of medical terminology. Embodiments of thepresent invention are not limited to using the SNOMED CT medicalhierarchy. Because each use of a clinical concept within the electronicmedical record may be indexed, the same clinical concept may be indexedseveral times. The scores given to each use of a clinical concept may bedifferent.

Continuing with FIG. 9, clinical concepts 912 include “myocardialinfarction” 915, “myocardial infarction” 916, “repair of hernia ofabdominal wall” 917, and “gastrointestinal ulcer” 918. Each clinicalconcept includes a series of attributes that are determined as describedpreviously. The truth status 920, clinical-usage context 930,patient-subject status 940, clinical concept type 950, specificityfactor 960, and document importance 970 are all recorded. Finally, theclinical-importance score 980 for each clinical concept is recorded. Inthe embodiment shown in FIG. 9, the clinical-importance score issummarized as a category of high, medium, or low. In another embodiment,the clinical-importance score could be recorded as a numerical valuethat is the result of a calculation.

Turning now to FIG. 4, a method 400 of finding information in electronicmedical record is shown, in accordance with an embodiment of the presentinvention. At step 410, a search query is received from a clinician. Aninterface to receive a search query is illustrated in FIG. 6. The searchinterface 600 includes a patient-banner bar 610 to displayidentification information for the patient whose electronic medicalrecord is being searched. The tab section 620 allows a clinician tonavigate to various functions of the search interface 600. Several tabsare includes in the tab section 620. The tab section 620 includes atoday tab 621, a documents search tab 622, a lab\vitals search tab 623,a graphing tab 624, a spark lines tab 625, and an IntelliStrip tab 626.The documents search tab 622 is shown as selected in FIG. 6. The searchinterface 600 includes a search input area 630. In an embodiment of thepresent invention, a list of suggested clinical concepts 632 ispresented in response to entering a partial search query 631. Eachsuggested clinical concept includes the name of the clinical concept andthe SNOMED CT identification number. The clinical concept “heartdisease” 633 is paired with ID number 634. The clinical concept “heartdisease due to ionizing radiation” 635 is paired with ID number 636. Theclinical concept “heart disease due to radiation” 637 is paired with IDnumber 638. The clinical concept “heart disease during pregnancy” 639 ispaired with ID number 640. The clinical concept “heart disease excluded”641 is paired with ID number 642. The clinical concept “heart disease inmother complicating pregnancy, childbirth and/or puerperium” is pairedwith ID number 644. The start of each clinical concept begins with“heart di.” the list of suggested clinical concepts 632 will show fewerclinical concepts as additional letters are typed into search input area630.

Returning to FIG. 4, at step 420, one or more components of theelectronic medical record that match the search query are identified. Acomponent is a section of text including one or more words/terms.Embodiments of the present invention allow a user to specify the type ofmatch required to return a search result. For example the user mayselect a text-only match. With a text-only match, stems of words in thesearch query must match stems of words within the electronic medicalrecord to generate a search result. In another embodiment, the queryterms are expanded to include medical synonyms of the query terms. Themedical synonyms are used as additional query terms to match componentsof an electronic medical record to generate a search result. Forexample, “heart attack” and “myocardial infarction” may be medicalsynonyms. A search result would be identified if either “heart attack”or “myocardial infarction” were found in the electronic medical record.In another embodiment, the user may request matches based on “child”clinical concepts. At query time, the clinical concept identified in thesearch query would be expanded to include “children” of the base conceptas defined by the associated clinical-hierarchy. These child conceptswould also be used to match clinical concepts identified within theelectronic medical record to produce a search result. In anotherembodiment, the user may search based on related concepts. With therelated concepts choice, the original query term is expanded to includeadditional related clinical concepts which may be matched to documentsin the patient's electronic medical record. The text-only matchingoption should return the least results and the related-concepts optionshould return the most results.

FIG. 6 illustrates an interface 600 for the user to select the matchingcriteria. The matching interface includes a text-only mode 652, aclinical concept mode 654, and related concepts mode 656. In oneembodiment, the search results are dynamically updated as the usertoggles between different matching criteria.

Returning to FIG. 4, at step 430, a query-responsiveness score isdetermined for each of the one or more components that match the searchquery. The query-responsiveness score indicates how responsive theindividual component is to the search query. As described previously,the query-responsiveness score may be calculated based on a combinationof the clinical-importance score of the matching clinical concepts withthe one or more components and the associated boost factors, asdescribed above. The calculation of a clinical-importance score and thevarious factors that may be considered when calculating theclinical-importance score have been described previously. Similarly,determining the boost factors has also been described previously.

At step 440, search results that communicate information describing eachof the one or more matching components are displayed. As describedpreviously, the matching components may be documents, portions ofdocuments, or other portions of the EMR. The search results aredisplayed ordered according to the query-responsiveness score assignedto each of the one or more components.

FIG. 7 illustrates the display of search results that are responsive toa submitted clinical concept. Result interface 700 includes a searchresult display area 720. The search results display area 720 includestwo search results, both of which are documents. Though not shown inFIG. 7, the search results may be components of a document. Each searchresult is displayed with an expansion column 726. Selecting theexpansion button shows other documents that are similar to searchresult. Each search result also has a rank 728, a query-responsivenessscore 730, an document age 732, a date 734, clinical-context information736, a document title 738, the clinician that authored or authenticatedthe search result 740, and text summarizing the search result 742. Thefirst search result is displayed first because it has the highestquery-responsiveness score, which in this case is 1.00 746. The highestranked search result is designated as number 1 744 in the list. Thedocument age is 3 yrs 748, the date of the document is Oct. 4, 2007 750,the clinical-usage context of the document 752 is “Cardio Assoc. ofOlathe/MD Consult.” The title of the document 754 is “Cardiac CathEvaluation.” The clinician is Tom Clark 756. The text excerpts 722 forthe first search results includes use of the clinical concept heartdisease 633. The search results are based on matching criteria ofrelated concepts 656. Clinical concepts related to heart disease areoutlined in the text excerpts. The outlined text includes “heartdisease” 758, “heart disease” 759, “heart disease” 760, “coronary arterydisease” 761, and “occluded right coronary artery” 762.

The second search result is designated as search result number two 768,and has a query-responsiveness score 769 of 0.540. The document age is 3yrs 770, and the date is Oct. 4, 2007. The clinical-usage context is“Beacon Health/MD Consult” 772 and the title of the document is “Cardconsult: exertional angina” 773. The clinician responsible for thesecond search result is Jordan Jones 774. The text excerpts 724 showoutlined text that matches the search query 733. The outlined textincludes “heart disease” 775, “coronary artery disease” 776, “angina”777, “angina” 778, “coronary artery disease” 779, “heart disease” 780,and “sinus BR” 781.

The result interface 700 also includes a filter interface 785. Thefilter interface 785 allows the clinician to filter by year 786,encounter locations 787, or document class 788. The number of searchresults that match a particular filter criteria are displayed inparentheses adjacent to the suggested filter criteria. For example, 17search results are available for the year 2007. The result interface 700also includes query reset button 710 that allows the user to submit anew query. A sort-documents-by-date button 712 is also included in thesearch result interface 700.

Turning now to FIG. 5, a method 500 of searching an electronic medicalrecord for a selected clinical concept is provided, in accordance withan embodiment of the present invention. At step 510, a search query thatincludes a clinical concept is received from a user. As describedpreviously, the clinical concept describes any aspect of a person'shealth condition, or any object, action, attribute or idea that isrelated to a health condition. Examples include: diseases; symptoms;clinical observations and findings; diagnostic tests; diagnostic ortherapeutic procedures; organisms, substances, devices or productsrelated to health conditions; anatomic structures including genomic;phenotypic expression; behavior, family and social context related tohealth conditions; risk factors and outcomes; facilities and careproviders. For example, heart disease and a heart attack are examples ofclinical concepts. The search query may be received through aninterface, such as search interface 600 described above with referenceto FIG. 6. The clinical concept may be entered directly into the searchinterface word-for-word. In another embodiment, the clinician selectsthe clinical concept from a list of clinical concepts. In anotherembodiment, clinical concepts are derived from natural language textsubmitted into the search interface. The derivation of clinical conceptsfrom natural language text may utilize a medical synonym dictionary. Anyand all such variations, and any combination thereof, are contemplatedto be within the scope of embodiments of the present invention.

At step 520, one or more uses of the clinical concept from the searchquery are identified in the electronic medical record. The electronicmedical record is an electronic description of a medical history for apatient and is stored on one or more computer-readable media. In oneembodiment, the clinical concepts within a medical record had previouslybeen extracted and indexed. In this case, the index is searched forclinical concepts that match the clinical concept within the searchquery. As described previously the clinical concept from the searchquery could be an expanded-clinical concept.

At step 530, a query-responsiveness score is determined for eachdocument within the electronic medical record that uses the clinicalconcept contained within the user's query. The query-responsivenessscore describes how important a particular document or component islikely to be to the clinician. As described previously, thequery-responsiveness score may be determined based on the combination ofclinical-importance scores for each of the one more uses of the clinicalconcept and a set of boost factors calculated based on the expansions ofthe search query submitted by the clinician. The factors, such asclinical-usage context, specificity factor, frequency, truth status, andothers that may be used to calculate the clinical-importance score havebeen described previously. Similarly, the factors utilized to calculatethe boost factors have also been described previously. For example, thecloseness-boost factor may be increased when the clinician thatsubmitted the search query is in the same category of role as theclinician associated with the use of the clinical concept.

At step 540, search results are displayed based on thequery-responsiveness score associated with each of the one or more usesof the clinical concept. The search result with the highestquery-responsiveness score may be displayed first. Search resultsassociated with lower query-responsiveness scores may be displayedsubsequently.

As can be seen, embodiments of the present invention allow a user tosubmit a search query through an interface and return search resultsfrom an electronic medical record that are responsive to the searchquery. The search results by default will be ordered according to aquery-responsiveness score so that the clinician will find the mostimportant document at the top of the list.

It will be understood that certain features and subcombinations are ofutility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims. Notall steps listed in the various figures need be carried out in thespecific order described.

1. One or more non-transitory computer-storage media having computerexecutable instructions embodied thereon that when executed by acomputer perform a method of finding information in an electronicmedical record, the method comprising: receiving a search query from auser to search the electronic medical record, wherein the electronicmedical record is associated with a patient, and wherein the electronicmedical record includes a plurality of electronic documents thatdescribe a medical history for the patient and is stored on thecomputer-storage media; identifying one or more components of theelectronic medical record that contain text that matches the searchquery, wherein each of the one or more components is a section of textwithin the electronic medical record that includes one or more words;determining a query-responsiveness score for each of the one or morecomponents that match the search query, wherein the query-responsivenessscore indicates how responsive an individual component is to the searchquery, wherein determining the query-responsiveness score of each of theone or more components further comprises: determining a patient-subjectstatus for each particular-clinical concept recited in the one or morecomponents, wherein the patient-subject status indicates whether thepatient is an object of a particular-clinical concept recited in aparticular component of the one or more components; determining a truthstatus for said each particular-clinical concept recited in the one ormore components, wherein the truth status indicates whether said eachparticular-clinical concept was expressed positively, negatively,ambiguously, or unknown; determining a clinical-usage context for eachparticular-clinical concept recited in the one or more components,wherein the clinical-usage context describes how the clinical conceptwas used in the component; determining a document-importance factor foreach particular-clinical concept recited in the one or more components,wherein the document-importance factor measures the relevance of theparticular-clinical concept to the main subject of a particular documentby analyzing other clinical concepts that are used in the particulardocument with the particular-clinical concept; determining a specificityfactor for each particular-clinical concept recited in the one or morecomponents based on a degree of narrowness for a scope of said eachparticular-clinical concept; and presenting search results thatcommunicate information describing each of the one or more components,wherein the search results are displayed ordered according to thequery-responsiveness score assigned to each of the one or morecomponents.
 2. The media of claim 1, wherein the method further includesreceiving a selection of a search mode, wherein the search mode is setto match related concepts, and wherein the one or more components of theelectronic medical record matches the search query when a clinicalconcept conveyed by the search query matches at least one relatedclinical concept recited in the one or more components of the electronicmedical record, and wherein the clinical concept describes any aspect ofa person's health condition.
 3. The media of claim 2, whereindetermining the query-responsiveness score of each of the one or morecomponents further includes: wherein the query-responsiveness score forthe individual component is increased when the patient-subject statusfor one or more of the clinical concepts identified within the componentis affirmative, when the truth status for one or more of the clinicalconcepts identified within the component equals positive, when theclinical-usage context for one or more of the clinical conceptsidentified within the component directly relates to the patient, whenthe document-importance factor is high, and when the specificity factorfor one or more of the clinical concepts identified within the componentis high.
 4. The media of claim 1, wherein the method further includesdisplaying one or more filter options that allows the user to filter thesearch results, wherein the one or more filter options are based on oneor more of a clinical-concept in an individual search result, anaggregation of related clinical concepts, a clinical facility associatedwith the individual search result, and a document class associated withthe individual search result.
 5. The media of claim 1, wherein themethod further includes receiving a selection of a search mode, whereinthe search mode is a medical synonym match, wherein the one or morecomponents of the electronic medical record matches the search querywhen at least a medical synonym of the one or more words in the searchquery is found within the one or more components of the electronicmedical record, and wherein the medical synonyms match search modeincludes matching two or more words or phrases that have a similarmedical meaning.
 6. The media of claim 1, wherein the method furtherincludes: expanding one or more primary clinical concepts recited in thesearch query to additional related clinical concepts, thereby generatinga plurality of expanded clinical concepts to match with a particular oneof the one or more components of the electronic medical record;assigning a boost factor to each combination of an expanded clinicalconcept and the particular matching component of the electronic medicalrecord, wherein the boost factor is higher when the expanded clinicalconcept is close to the one or more primary clinical concepts on aclinical-concept ontology and lower when the expanded clinical conceptis remote from the one or more primary clinical concepts; and whereinthe boost factor is used to calculate the query-responsiveness score forsaid each combination of the expanded clinical concept and theparticular-clinical concepts contained within the matching component. 7.The media of claim 1, wherein the method further includes increasing thequery-responsiveness score when a role of the user who submitted thesearch query is in the same category as the role of a person thatcreated a matching component.
 8. One or more non-transitorycomputer-storage media having computer executable instructions embodiedthereon that when executed by a computer perform for performing a methodof searching an electronic medical record for clinical concepts, themethod comprising: receiving a search query that includes a clinicalconcept from a user to search the electronic medical record, wherein theclinical concept is an aspect related to a person's health, wherein theelectronic medical record is associated with a patient, and wherein theelectronic medical record includes a plurality of electronic documentsthat describe a medical history for the patient and is stored on thecomputer-storage media; identifying one or more uses of the clinicalconcept in the electronic medical record, wherein a use of a clinicalconcept is a single occurrence of the clinical concept within theelectronic medical record, wherein the documents in the electronicmedical record describe the patient's medical history, and wherein theelectronic medical record is stored on the computer-storage media;determining a clinical-importance score for each of the one or more usesof the clinical concept, wherein the clinical-importance scorequantifies how important each use of the clinical concept is likely tobe to the user querying the electronic medical record, and wherein theclinical-importance score is calculated based on the use of the clinicalconcept within the electronic medical record and without reference toany specific search query; determining at least one boost factor foreach of the one or more uses of the clinical concept, wherein the atleast one boost factor is based on the search query and the user,wherein the at least one boost factor includes a role-boost factor,wherein each role-boost factor is based on a role of the user thatsubmitted the search query and a role of a person that generated theparticular use of the clinical concept in the electronic medical record,and wherein the particular role-boost factor is increased when the roleof the user is in the same category as the role of the person associatedwith the use of the clinical concept within the electronic medicalrecord; calculating a query-responsiveness score for each documentwithin the electronic medical record in which the clinical concept isused by combining the one or more clinical-importance scores with theone or more boost factors; and presenting search results based on thequery-responsiveness score associated with each said document within theelectronic medical record in which the one or more uses of a clinicalconcept was identified, wherein the search results are displayed orderedaccording to the query-responsiveness score.
 9. The media of claim 8,wherein the method further includes: determining a clinical-usagecontext for each use of the clinical concept in the electronic medicalrecord, wherein the clinical-usage context describes how the clinicalconcept was used in each document of the electronic medical record;determining a truth status for each use of the clinical concept in theelectronic medical record, wherein the truth status indicates whetherthe clinical concept was expressed in as one of positively, negatively,ambiguously, and unknown; and wherein the clinical-importance score forsaid each of the one or more uses of the clinical concept is based onthe clinical-usage context and the truth status for said each of the oneor more uses of the clinical concept.
 10. The media of claim 9, whereinthe method further includes: determining a patient-subject status foreach use of the clinical concept in the documents of the electronicmedical record, wherein the patient-subject status indicates whether ornot the patient is an object of the particular use of the clinicalconcept; and wherein the clinical-importance score for said each of theone or more uses of the clinical concept in said each of the one or moredocuments in the electronic medical record is based on theclinical-usage context, the truth status, and the patient-subject statusfor said each of the one or more uses of the clinical concept.
 11. Themedia of claim 10, wherein the method further includes: determining aspecificity factor for the particular use of the clinical concept basedon a degree of narrowness for a scope of the particular use of theclinical concept; determining a document-importance factor for theparticular use of the clinical concept, wherein the document-importancefactor measures the relevance of the particular use of the clinicalconcept to the main subject of a particular document by analyzing otherclinical concepts that are used in the particular document with theparticular use of the clinical concept; and wherein theclinical-importance score for said each of the one or more uses of theclinical concept is based on the clinical-usage context, the truthstatus, the patient-subject status, the document-importance factor, andthe specificity factor for the particular use.
 12. The media of claim 8,wherein the at least one boost factor includes a clinical-facility-boostfactor, wherein the clinical-facility-boost factor is based on aclinical facility associated with the user that submitted the searchquery and the clinical facility associated with a use of the clinicalconcept in the electronic medical record, and wherein aclinical-facility-boost factor is increased for the use of the clinicalconcept in the electronic medical record when the use of the clinicalconcept in the electronic medical record is in a portion of theelectronic medical record that is associated with the clinical facilityfrom which the user submitted the search query.
 13. The media of claim8, wherein the method further includes: expanding the clinical conceptrecited in the search query into additional related clinical concepts,thereby generating a plurality of expanded clinical concepts to match toeach of the uses of the plurality of expanded clinical concepts withindocuments in the electronic medical record; assigning a closeness-boostfactor to each combination of an expanded-clinical concept and amatching use of the expanded-clinical concept within a document in theelectronic medical record, wherein the closeness-boost factor is higherwhen the expanded-clinical concept is close to the clinical concept on aclinical-concept ontology and lower when the expanded clinical conceptis remote from the clinical concept; and wherein the at least one boostfactor includes the closeness-boost factor.